=Paper=
{{Paper
|id=Vol-2980/paper412
|storemode=property
|title=Flexible and
Extensible Competency Management with Knowledge Graphs
|pdfUrl=https://ceur-ws.org/Vol-2980/paper412.pdf
|volume=Vol-2980
|authors=Nicolas Heist, Peter Haase
|dblpUrl=https://dblp.org/rec/conf/semweb/Heist021
}}
==Flexible and
Extensible Competency Management with Knowledge Graphs==
https://ceur-ws.org/Vol-2980/paper412.pdf
Flexible and Extensible Competency
Management with Knowledge Graphs
Nicolas Heist1,2†[0000−0002−4354−9138] , Peter Haase1 , and Simon Scerri1
1
metaphacts GmbH, Walldorf, Germany
{nh,ph,simon.scerri}@metaphacts.com
2
Data and Web Science Group, University of Mannheim, Germany
nico@informatik.uni-mannheim.de
Abstract. Especially in the diverse and fast-paced field of Artificial In-
telligence it is imperative to have a clear picture of relevant competencies
and how they are distributed within or over organisations. For this pur-
pose, we have developed a generic competency ontology that can be used
to describe competencies of people and organisational structures in the
Artificial Intelligence domain. The ontology is embedded in an applica-
tion to create, manage, and utilize a Competency Knowledge Graph. In
our presentation we show concrete application scenarios, advantages, and
challenges.
Competency Management With a growing team, organisation, or consor-
tium size, it becomes increasingly difficult for individuals to get an overview of
their business partners’ or co-workers’ competencies. This is problematic in var-
ious industry settings, but especially in the field of Artificial Intelligence, which
is strongly driven by innovation and where skills and know-how are quick to
change and evolve. In medium and large-sized companies, where many projects
are conducted and where AI technologies are increasingly involved, individuals
often need to quickly identify experts of specific topics in their extended net-
work, as well as existing alternative solutions and general know-how produced
by their peers.
The value of choosing a Knowledge Graph paradigm for extensive information
retrieval is widely documented [2]. We therefore target a Competency Knowledge
Graph whose setup, population and upkeep can be achieved with minimal effort,
while also exploiting existing open data to further augment it at virtually no cost.
Our implemented solution demonstrates how the following common business
scenarios can be supported:
– Browsing through AI competencies, their interrelations and relevant publica-
tions in order to resolve an information need. For instance, given a concrete
business problem, one can find the competencies relevant to solve it.
†
This work was created during an internship at metaphacts GmbH.
Copyright © 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
2 N. Heist et al.
– Discover persons or organisations having relevant competencies; suggest re-
lated competencies to identify knowledge gaps; find completed projects to
foster reuse (based on competencies defined using the ontology).
The Competency Ontology The competency ontology provides the three
generic classes Competency, CompetencyHolder, and CompetencyTarget. These
are supposed to be extended by sub-classes in order to describe a concrete sce-
nario. A Competency is a particular skill or piece of know-how that one might
possess or apply. To be able to describe competencies in the AI domain, we in-
corporate the data from AI-KG [1], which is a knowledge graph automatically
extracted from the Microsoft Academic Graph.3 The graph describes 850K re-
search entities (i.e. specific Tasks, Methods, Metrics, and Materials in the AI
domain) and their relations. We include those as sub-classes of Comptency in
our ontology. CompetencyHolder s are entities that possess or apply competen-
cies. In our case, those are persons and organisations. To model them, we re-use
definitions from existing ontologies like FOAF4 and ORG5 as sub-classes. Com-
petencyTargets serve to describe the context in which concrete competencies are
applied in. In a business setting, these are concepts like projects, processes, or
applications. Here, we again rely on well-established ontologies like FOAF.
Application Design We use metaphactory [3] for the design of the com-
petency management application as it provides several advantages; primarily
its ability to guide the input of competency descriptions based on the underly-
ing ontology through intuitive components like ontology-driven semantic forms
and search. Furthermore, metaphactory’s low-code approach makes it possible
to rapidly develop custom dashboards. In general, a knowledge graph-based ap-
proach to define competencies reduces effort with respect to data integration,
scalability and flexibility. Apart from the re-use of established ontologies to cover
the modeling requirements, external data sources can also be tapped using fed-
erated queries. For instance, we display information about publications of AI
competencies from an external SPARQL endpoint of the Microsoft Academic
Graph (as it would be too large to integrate). The approach is also easily ex-
tendable to domains outside of AI due to the open nature of the RDF data model
and metaphactory’s low-code dashboarding capabilities.
References
1. Dessı̀, D., et al.: AI-KG: an automatically generated knowledge graph of artificial in-
telligence. In: International Semantic Web Conference. pp. 127–143. Springer (2020)
2. Galkin, M., Auer, S., Vidal, M.E., Scerri, S.: Enterprise knowledge graphs: A se-
mantic approach for knowledge management in the next generation of enterprise
information systems. In: ICEIS (2). pp. 88–98 (2017)
3. Haase, P., Herzig, D.M., Kozlov, A., Nikolov, A., Trame, J.: metaphactory: A plat-
form for knowledge graph management. Semantic Web 10(6), 1109–1125 (2019)
3
https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/
4
http://www.foaf-project.org
5
https://www.w3.org/TR/vocab-org/